Foundations of Machine Learning second edition – Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalkar

This book was written for anyone who wishes to explore deep learning from scratch or broaden their understanding of deep learning. Whether you’re a practicing machine-learning engineer, a software developer,
or a college student, you’ll find value in these pages.

This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core
ideas of machine learning and deep learning.

You’ll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems. The code examples use the Python deep-learning framework Keras, with TensorFlow as a backend engine. Keras, one of the
most popular and fastest-growing deep-learning frameworks, is widely recommended as the best tool to get started with deep learning.

After reading this book, you’ll have a solid understand of what deep learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving machine-learning problems, and you’ll know how to address commonly encountered issues. You’ll be able to use Keras to tackle real-world problems ranging from computer vision to natural-language processing: image classification, timeseries forecasting, sentiment analysis, image and text generation,
and more.

Related posts:

Statistical Methods for Machine Learning - Disconver how to Transform data into Knowledge with Pytho...
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Introduction to Deep Learning - Eugene Charniak
Machine Learning with spark and python - Michael Bowles
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Data Science and Big Data Analytics - EMC Education Services
R Deep Learning Essentials - Dr. Joshua F.Wiley
Artificial Intelligence - A Very Short Introduction - Margaret A.Boden
Deep Learning with Theano - Christopher Bourez
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Java Deep Learning Essentials - Yusuke Sugomori
Deep Learning with PyTorch - Vishnu Subramanian
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning in Python - LazyProgrammer
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Building Chatbots with Python Using Natural Language Processing and Machine Learning - Sumit Raj
Learn Keras for Deep Neural Networks - Jojo Moolayil
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Fundamentals of Deep Learning - Nikhil Bubuma
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Neural Networks and Deep Learning - Charu C.Aggarwal
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Python Data Structures and Algorithms - Benjamin Baka